Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
7.3
3.4
Journals
Sensors
Special Issues
Sensor Technologies for Seismic Monitoring
sensors-logo
Submit to Special Issue Submit Abstract to Special Issue Review for Sensors Propose a Special Issue

Journal Menu

► Journal Menu

Journal Browser

► Journal Browser
share announcement

Sensor Technologies for Seismic Monitoring

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Sensing and Imaging".

Deadline for manuscript submissions: 20 January 2025 | Viewed by 3412

Special Issue Editors

Prof. Dr. Yibo Wang

E-Mail Website
Guest Editor
Institute of Geology and Geophysics, Chinese Academy of Sciences, Beijing 100029, China
Interests: seismic monitoring instruments and methods
Prof. Dr. Wentao Zhang

E-Mail Website
Guest Editor
Institute of Seimiconductors, Chinese Academy of Sciences, Beijing 100083, China
Interests: fiber optic sensor technology; optoelectronic sensor and application
Dr. Qingwen Liu

E-Mail Website1 Website2
Guest Editor
School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China
Interests: fiber-optic sensors and applications
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Danping Cao

E-Mail Website
Guest Editor
School of Geosciences, China University of Petroleum (Huadong), Qingdao 266580, China
Interests: geophysics of oil and gas reservoirs; seismic wave propagation in viscoelastic media

Special Issue Information

Dear Colleagues,

Recently, we have seen a growing interest in seismic monitoring, which safeguards human lives through the comprehension of such phenomena. Advances in technology have allowed more and more sophisticated seismic networks to be designed; modern seismic networks based on new sensor technologies can detect a massive number of earthquakes, generating an extremely large dataset for analysis. The application of technology such as ultra-sensitive MEMS-based and interferometric accelerometers, distributed optical fiber sensing (DOFS), and distributed acoustic sensing (DAS) based on fiber optics technology has dramatically improved the seismic monitoring capability. Moreover, there are many geophysical phenomena that we are able to record with seismic networks.

This Special Issue, therefore, aims to highlight advances in sensor technologies for seismic monitoring and data analysis methods. Original research and review articles on advanced techniques of seismic monitoring are welcome.

Prof. Dr. Yibo Wang
Prof. Dr. Wentao Zhang
Dr. Qingwen Liu
Prof. Dr. Danping Cao
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sensors is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • seismic sensors
  • seismic monitoring instruments
  • seismic monitoring data analysis methods
  • seismic monitoring applications
  • AI for seismic sensing and monitoring
  • fiber-optic sensors for seismic applications

Benefits of Publishing in a Special Issue

  • Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • e-Book format: Special Issues with more than 10 articles can be published as dedicated e-books, ensuring wide and rapid dissemination.

Further information on MDPI's Special Issue polices can be found here.

Published Papers (3 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

18 pages, 6536 KiB  
Article
Strong Interference Elimination in Seismic Data Using Multivariate Variational Mode Extraction
by Zhichao Yu, Yuyang Tan and Yiran Lv
Sensors 2024, 24(22), 7399; https://doi.org/10.3390/s24227399 - 20 Nov 2024
Viewed by 355
Abstract
Seismic data acquired in the presence of mechanical vibrations or power facilities may be contaminated by strong interferences, significantly decreasing the data signal-to-noise ratio (S/N). Conventional methods, such as the notch filter and time-frequency transform method, are usually inadequate for suppressing non-stationary interference [...] Read more.
Seismic data acquired in the presence of mechanical vibrations or power facilities may be contaminated by strong interferences, significantly decreasing the data signal-to-noise ratio (S/N). Conventional methods, such as the notch filter and time-frequency transform method, are usually inadequate for suppressing non-stationary interference noises, and may distort effective signals if overprocessing. In this study, we propose a method for eliminating mechanical vibration interferences in seismic data. In our method, we extended the variational mode extraction (VME) technique to a multivariate form, called multivariate variational mode extraction (MVME), for synchronous analysis of multitrace seismic data. The interference frequencies are determined via synchrosqueezing-based time-frequency analysis of process recordings; their corresponding modes are extracted and removed from seismic data using MVME with optimal balancing factors. We used synthetic data to investigate the effectiveness of the method and the influence of tuning parameters on processing results, and then applied the method to field datasets. The results have demonstrated that, compared with the conventional methods, the proposed method could effectively suppress the mechanical vibration interferences, improve the S/Ns and enhance polarization analysis of seismic signals. Full article
(This article belongs to the Special Issue Sensor Technologies for Seismic Monitoring)
►▼ Show Figures

Figure 1

13 pages, 4265 KiB  
Communication
Moho Imaging with Fiber Borehole Strainmeters Based on Ambient Noise Autocorrelation
by Guoheng Qi, Wenzhu Huang, Xinpeng Pan, Wentao Zhang and Guanxin Zhang
Sensors 2024, 24(13), 4252; https://doi.org/10.3390/s24134252 - 30 Jun 2024
Viewed by 779
Abstract
Moho tomography is important for studying the deep Earth structure and geodynamics, and fiber borehole strainmeters are broadband, low-noise, and attractive tools for seismic observation. Recently, many studies have shown that fiber optic seismic sensors can be used for subsurface structure imaging based [...] Read more.
Moho tomography is important for studying the deep Earth structure and geodynamics, and fiber borehole strainmeters are broadband, low-noise, and attractive tools for seismic observation. Recently, many studies have shown that fiber optic seismic sensors can be used for subsurface structure imaging based on ambient noise cross-correlation, similar to conventional geophones. However, this array-dependent cross-correlation method is not suitable for fiber borehole strainmeters. Here, we developed a Moho imaging scheme for the characteristics of fiber borehole strainmeters based on ambient noise autocorrelation. S-wave reflection signals were extracted from the ambient noise through a series of processing steps, including phase autocorrelation (PAC), phase-weighted stacking (PWS), etc. Subsequently, the time-to-depth conversion crustal thickness beneath the station was calculated. We applied our scheme to continuous four-component recordings from four fiber borehole strainmeters in Lu’an, Anhui Province, China. The obtained Moho depth was consistent with the previous research results. Our work shows that this method is suitable for Moho imaging with fiber borehole strainmeters without relying on the number of stations. Full article
(This article belongs to the Special Issue Sensor Technologies for Seismic Monitoring)
►▼ Show Figures

Figure 1

27 pages, 20869 KiB  
Article
Seismic Monitoring of a Deep Geothermal Field in Munich (Germany) Using Borehole Distributed Acoustic Sensing
by Jérôme Azzola and Emmanuel Gaucher
Sensors 2024, 24(10), 3061; https://doi.org/10.3390/s24103061 - 11 May 2024
Viewed by 1260
Abstract
Geothermal energy exploitation in urban areas necessitates robust real-time seismic monitoring for risk mitigation. While surface-based seismic networks are valuable, they are sensitive to anthropogenic noise. This study investigates the capabilities of borehole Distributed Acoustic Sensing (DAS) for local seismic monitoring of a [...] Read more.
Geothermal energy exploitation in urban areas necessitates robust real-time seismic monitoring for risk mitigation. While surface-based seismic networks are valuable, they are sensitive to anthropogenic noise. This study investigates the capabilities of borehole Distributed Acoustic Sensing (DAS) for local seismic monitoring of a geothermal field located in Munich, Germany. We leverage the operator’s cloud infrastructure for DAS data management and processing. We introduce a comprehensive workflow for the automated processing of DAS data, including seismic event detection, onset time picking, and event characterization. The latter includes the determination of the event hypocenter, origin time, seismic moment, and stress drop. Waveform-based parameters are obtained after the automatic conversion of the DAS strain-rate to acceleration. We present the results of a 6-month monitoring period that demonstrates the capabilities of the proposed monitoring set-up, from the management of DAS data volumes to the establishment of an event catalog. The comparison of the results with seismometer data shows that the phase and amplitude of DAS data can be reliably used for seismic processing. This emphasizes the potential of improving seismic monitoring capabilities with hybrid networks, combining surface and downhole seismometers with borehole DAS. The inherent high-density array configuration of borehole DAS proves particularly advantageous in urban and operational environments. This study stresses that realistic prior knowledge of the seismic velocity model remains essential to prevent a large number of DAS sensing points from biasing results and interpretation. This study suggests the potential for a gradual extension of the network as geothermal exploitation progresses and new wells are equipped, owing to the scalability of the described monitoring system. Full article
(This article belongs to the Special Issue Sensor Technologies for Seismic Monitoring)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-3
Back to TopTop